
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm

from sklearn.model_selection import train_test_split
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
from scipy.stats import skew
from scipy.stats import boxcox
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from scipy.cluster.hierarchy import linkage, dendrogram
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from scipy import stats
from scipy.stats import probplot

sns.set_context('poster')


breast_cancer = pd.read_csv('MS4S16_Dataset.csv')


breast_cancer.head(5)


breast_cancer.tail(5)


breast_cancer.describe()


breast_cancer['diagnosis'].value_counts()

diagnosis
B    356
M    212
Name: count, dtype: int64


# grouping all null values together to ascertain the true number of null values
replace_values = ['NaN', 'NA', 'None', -999, 0, '-', '']


breast_cancer.replace(replace_values, np.nan, inplace=True)


breast_cancer.isnull().sum()

id                           3
diagnosis                    3
radius_mean                  5
texture_mean               151
perimeter_mean               4
area_mean                    5
smoothness_mean              3
compactness_mean             4
concavity_mean              18
concave points_mean         24
symmetry_mean                3
fractal_dimension_mean      12
radius_se                    6
texture_se                   8
perimeter_se                 3
area_se                      6
smoothness_se                6
compactness_se               7
concavity_se                22
concave points_se           23
symmetry_se                  8
fractal_dimension_se         7
radius_worst                13
texture_worst               21
perimeter_worst              6
area_worst                   4
smoothness_worst             9
compactness_worst            4
concavity_worst             17
concave points_worst        20
symmetry_worst               4
fractal_dimension_worst     13
dtype: int64


breast_cancer.dropna(subset=['id'], inplace=True)


breast_cancer.drop('texture_mean',axis=1, inplace=True)


breast_cancer.drop('id',axis=1, inplace=True)


breast_cancer.duplicated().sum()

2


breast_cancer.drop_duplicates(inplace=True)


X_train, X_test, y_train, y_test = train_test_split( #split data
    breast_cancer.drop('diagnosis', axis=1),
    breast_cancer['diagnosis'],
    test_size=0.3,
    random_state=42
)

columns_to_fill = [
    'radius_mean', 'perimeter_mean', 'area_mean', 'compactness_mean', 'concavity_mean',
    'concave points_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'area_se',
    'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
    'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst',
    'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst',
    'symmetry_worst', 'fractal_dimension_worst'
]

# SimpleImputer with strategy='median'
imputer = SimpleImputer(strategy='median')

# Group by the target variable and apply imputation within each group
for category in y_train.unique():
    # Select rows for the current category
    category_indices_train = y_train[y_train == category].index
    category_indices_test = y_test[y_test == category].index
    
    # Fit on the training set and transform both training and test sets for the current category
    X_train.loc[category_indices_train, columns_to_fill] = imputer.fit_transform(X_train.loc[category_indices_train, columns_to_fill])
    X_test.loc[category_indices_test, columns_to_fill] = imputer.transform(X_test.loc[category_indices_test, columns_to_fill])


X_train.isnull().sum()

radius_mean                0
perimeter_mean             0
area_mean                  0
smoothness_mean            0
compactness_mean           0
concavity_mean             0
concave points_mean        0
symmetry_mean              0
fractal_dimension_mean     0
radius_se                  0
texture_se                 0
perimeter_se               0
area_se                    0
smoothness_se              0
compactness_se             0
concavity_se               0
concave points_se          0
symmetry_se                0
fractal_dimension_se       0
radius_worst               0
texture_worst              0
perimeter_worst            0
area_worst                 0
smoothness_worst           0
compactness_worst          0
concavity_worst            0
concave points_worst       0
symmetry_worst             0
fractal_dimension_worst    0
dtype: int64


X_train.describe()


X_train.median()

radius_mean                 13.290000
perimeter_mean              85.935000
area_mean                  546.350000
smoothness_mean              0.094895
compactness_mean             0.092350
concavity_mean               0.059105
concave points_mean          0.033770
symmetry_mean                0.178800
fractal_dimension_mean       0.061820
radius_se                    0.321750
texture_se                   1.108000
perimeter_se                 2.278500
area_se                     24.265000
smoothness_se                0.006353
compactness_se               0.020335
concavity_se                 0.026065
concave points_se            0.011095
symmetry_se                  0.018585
fractal_dimension_se         0.003212
radius_worst                14.940000
texture_worst               25.050000
perimeter_worst             98.320000
area_worst                 687.100000
smoothness_worst             0.129800
compactness_worst            0.209250
concavity_worst              0.219350
concave points_worst         0.098095
symmetry_worst               0.280500
fractal_dimension_worst      0.080075
dtype: float64


data = pd.concat([X_train, y_train], axis=1)

# List of columns for which you want to calculate the median
columns_to_check = [
    'radius_mean', 'perimeter_mean', 'area_mean', 'compactness_mean', 'concavity_mean',
    'concave points_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'area_se',
    'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
    'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst',
    'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst',
    'symmetry_worst', 'fractal_dimension_worst'
]

# Calculate median values by class
data.groupby(y_train)[columns_to_check].median()


X_test.isnull().sum()

radius_mean                0
perimeter_mean             0
area_mean                  0
smoothness_mean            0
compactness_mean           0
concavity_mean             0
concave points_mean        0
symmetry_mean              0
fractal_dimension_mean     0
radius_se                  0
texture_se                 0
perimeter_se               0
area_se                    0
smoothness_se              0
compactness_se             0
concavity_se               0
concave points_se          0
symmetry_se                0
fractal_dimension_se       0
radius_worst               0
texture_worst              0
perimeter_worst            0
area_worst                 0
smoothness_worst           0
compactness_worst          0
concavity_worst            0
concave points_worst       0
symmetry_worst             0
fractal_dimension_worst    0
dtype: int64


X_test.describe()


X_test.median()

radius_mean                 13.375000
perimeter_mean              86.415000
area_mean                  552.300000
smoothness_mean              0.097165
compactness_mean             0.094490
concavity_mean               0.064900
concave points_mean          0.033065
symmetry_mean                0.179400
fractal_dimension_mean       0.061280
radius_se                    0.322000
texture_se                   1.133500
perimeter_se                 2.285500
area_se                     24.565000
smoothness_se                0.006463
compactness_se               0.020350
concavity_se                 0.024760
concave points_se            0.010695
symmetry_se                  0.018975
fractal_dimension_se         0.003204
radius_worst                14.885000
texture_worst               25.765000
perimeter_worst             97.385000
area_worst                 667.450000
smoothness_worst             0.134800
compactness_worst            0.226250
concavity_worst              0.255650
concave points_worst         0.104250
symmetry_worst               0.288250
fractal_dimension_worst      0.079545
dtype: float64


data = pd.concat([X_test, y_test], axis=1)

# List of columns for which you want to calculate the median
columns_to_check = [
    'radius_mean', 'perimeter_mean', 'area_mean', 'compactness_mean', 'concavity_mean',
    'concave points_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'area_se',
    'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
    'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst',
    'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst',
    'symmetry_worst', 'fractal_dimension_worst'
]

# Calculate median values by class
data.groupby(y_test)[columns_to_check].median()


y_train.describe()

count     396
unique      2
top         B
freq      249
Name: diagnosis, dtype: object


y_test.describe()

count     170
unique      2
top         B
freq      106
Name: diagnosis, dtype: object


target_variable = 'diagnosis'

# Perform one-hot encoding on the target variable for the training set
y_train_encoded = pd.get_dummies(y_train, columns=[target_variable], drop_first=True)

# Convert boolean values to integers (0 or 1)
y_train_encoded = y_train_encoded.astype(int)

# Rename the column to 'diagnosis'
y_train_encoded = y_train_encoded.rename(columns={'M': target_variable})

# Replace the original 'diagnosis' column with the encoded values
y_train = y_train_encoded

y_test_encoded = pd.get_dummies(y_test, columns=[target_variable], drop_first=True)

# Convert boolean values to integers (0 or 1)
y_test_encoded = y_test_encoded.astype(int)

# Rename the column to 'diagnosis'
y_test_encoded = y_test_encoded.rename(columns={'M': target_variable})

# Replace the original 'diagnosis' column with the encoded values
y_test = y_test_encoded


y_train.head()


y_test.head()


class_counts = pd.value_counts(y_train.values.flatten())

# Display the counts
print("Class Counts:")
print(class_counts)

Class Counts:
0    249
1    147
Name: count, dtype: int64


class_counts = pd.value_counts(y_test.values.flatten())

# Display the counts
print("Class Counts:")
print(class_counts)

Class Counts:
0    106
1     64
Name: count, dtype: int64


for variable in X_train.columns:
    plt.hist(X_train[variable], bins=20, color='skyblue', edgecolor='black', alpha=0.7)
    plt.grid(axis='y', linestyle='--', alpha=0.7)
    plt.title(f'Histogram for {variable} (Training Set)', fontsize=10)
    plt.xlabel(variable, fontsize=8)
    plt.ylabel('Frequency', fontsize=8)
    skewness = skew(X_train[variable])
    plt.text(0.95, 0.9, f'Skewness: {skewness:.2f}', transform=plt.gca().transAxes, ha='right', va='top')
    plt.show()


for variable in X_test.columns:
    plt.figure(figsize=(6, 4))  # Adjust the figure size as needed
    plt.hist(X_test[variable], bins=20, color='skyblue', edgecolor='black', alpha=0.7)
    plt.grid(axis='y', linestyle='--', alpha=0.7)
    plt.title(f'Histogram for {variable} (Test Set)', fontsize=12)
    plt.xlabel(variable, fontsize=10)
    plt.ylabel('Frequency', fontsize=10)
    skewness = skew(X_test[variable])
    plt.text(0.95, 0.9, f'Skewness: {skewness:.2f}', transform=plt.gca().transAxes, ha='right', va='top', fontsize=10)
    plt.tight_layout()  # Adjust layout to prevent overlap
    plt.show()


features_to_transform = ['symmetry_mean', 'radius_se', 'perimeter_se', 'area_se', 
                         'concavity_se', 'fractal_dimension_se', 'perimeter_worst', 'area_worst']

# Copy original DataFrames
X_train_transformed = X_train.copy()
X_test_transformed = X_test.copy()

# Lists to store original and transformed skewness values for training and test sets
original_train_skewness = []
transformed_train_skewness = []
original_test_skewness = []
transformed_test_skewness = []

# Loop through selected features
for column in features_to_transform:
    if X_train[column].dtype != 'object':  # Check if the column is numerical
        # Transform training set
        transformed_variable_train, lambda_value_train = boxcox(X_train[column] + 1)
        X_train_transformed[column] = transformed_variable_train

        # Store skewness values
        original_train_skewness.append(skew(X_train[column]))
        transformed_train_skewness.append(skew(transformed_variable_train))

        # Plot original and transformed distributions for training set
        plt.figure(figsize=(10, 4))
        plt.subplot(1, 2, 1)
        plt.hist(X_train[column], bins=20, color='skyblue', edgecolor='black', alpha=0.7)
        plt.title(f'Original Distribution (Training Set - {column})', fontsize=10)

        plt.subplot(1, 2, 2)
        plt.hist(transformed_variable_train, bins=20, color='orange', edgecolor='black', alpha=0.7)
        plt.title(f'Transformed Distribution (Training Set - {column})', fontsize=10)

        plt.tight_layout()
        plt.show()
        
        # Transform test set using lambda value from training set
        transformed_variable_test = boxcox(X_test[column] + 1, lmbda=lambda_value_train)
        X_test_transformed[column] = transformed_variable_test

        # Store test set skewness values
        original_test_skewness.append(skew(X_test[column]))
        transformed_test_skewness.append(skew(transformed_variable_test))

        # Plot original and transformed distributions for test set
        plt.figure(figsize=(10, 4))
        plt.subplot(1, 2, 1)
        plt.hist(X_test[column], bins=20, color='skyblue', edgecolor='black', alpha=0.7)
        plt.title(f'Original Distribution (Test Set - {column})', fontsize=10)

        plt.subplot(1, 2, 2)
        plt.hist(transformed_variable_test, bins=20, color='orange', edgecolor='black', alpha=0.7)
        plt.title(f'Transformed Distribution (Test Set - {column})', fontsize=10)

        plt.tight_layout()
        plt.show()

# Print skewness values
pd.DataFrame({
    'Feature': features_to_transform,
    'Original Skewness (Train)': original_train_skewness,
    'Transformed Skewness (Train)': transformed_train_skewness,
    'Original Skewness (Test)': original_test_skewness,
    'Transformed Skewness (Test)': transformed_test_skewness
})


def plot_qq(feature_name, transformed_train, transformed_test):
    plt.figure(figsize=(8, 8))
    
    # Train set QQ plot
    ax1 = plt.subplot(2, 1, 1)
    probplot(transformed_train, dist="norm", plot=ax1)
    ax1.set_title(f'QQ Plot for {feature_name} (Train)', fontsize=12)
    ax1.tick_params(axis='both', which='major', labelsize=10)
    
    # Test set QQ plot
    ax2 = plt.subplot(2, 1, 2)
    probplot(transformed_test, dist="norm", plot=ax2)
    ax2.set_title(f'QQ Plot for {feature_name} (Test)', fontsize=12)
    ax2.tick_params(axis='both', which='major', labelsize=10)
    
    plt.tight_layout()
    plt.show()

# Loop through transformed features
for column in features_to_transform:
    plot_qq(column, X_train_transformed[column], X_test_transformed[column])


# Create a StandardScaler instance
scaler = StandardScaler()

# Fit the scaler on the training data and transform both the training and test data
X_train_scaled = scaler.fit_transform(X_train_transformed)
X_test_scaled = scaler.transform(X_test_transformed)


X_train_scaled_df = pd.DataFrame(X_train_scaled, columns=X_train.columns)
X_test_scaled_df = pd.DataFrame(X_test_scaled, columns=X_test.columns)


plt.figure(figsize=(46,46))
sns.heatmap(X_train_scaled_df.corr(), annot=True)

<Axes: >


variables = ['radius_worst', 
        'texture_worst',  
        'area_worst', 
        'smoothness_worst', 
        'compactness_worst', 
        'concavity_worst',
        'concave points_worst', 
        'fractal_dimension_worst']
X_train_scaled_df = X_train_scaled_df.drop(variables, axis=1)

variables = ['perimeter_mean',
        'perimeter_se', 
        'radius_mean', 
        'radius_se']
X_train_scaled_df = X_train_scaled_df.drop(variables, axis=1)

variables = ['concavity_mean',
        'concavity_se', 
        'concave points_mean', 
        'concave points_se']
X_train_scaled_df = X_train_scaled_df.drop(variables, axis=1)

variables = ['area_se',
        'compactness_se']
X_train_scaled_df = X_train_scaled_df.drop(variables, axis=1)


plt.figure(figsize=(46,46))
sns.heatmap(X_train_scaled_df.corr(), annot=True)

<Axes: >


variables = ['perimeter_worst']
X_train_scaled_df = X_train_scaled_df.drop(variables, axis=1)


plt.figure(figsize=(46,46))
sns.heatmap(X_train_scaled_df.corr(), annot=True)

<Axes: >


variables = ['radius_worst', 
        'texture_worst',  
        'area_worst', 
        'smoothness_worst', 
        'compactness_worst', 
        'concavity_worst',
        'concave points_worst', 
        'fractal_dimension_worst',
        'perimeter_mean',
        'perimeter_se', 
        'radius_mean', 
        'radius_se',
        'concavity_mean',
        'concavity_se', 
        'concave points_mean', 
        'concave points_se',
        'area_se',
        'compactness_se',
        'perimeter_worst']
X_test_scaled_df = X_test_scaled_df.drop(variables, axis=1)


plt.figure(figsize=(46,46))
sns.heatmap(X_test_scaled_df.corr(), annot=True)

<Axes: >


num_clusters = 2

# Apply KMeans clustering
kmeans = KMeans(n_clusters=num_clusters, random_state=42, n_init=10)
cluster_labels = kmeans.fit_predict(X_train_scaled_df)

# Calculate silhouette score
silhouette_score(X_train_scaled_df, cluster_labels)

C:\Users\Dell\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.
  warnings.warn(

0.25535282611289684


linkage_matrix = linkage(X_train_scaled_df, method='ward')

# Plot the dendrogram
plt.figure(figsize=(15, 8))
dendrogram(linkage_matrix, orientation='top', labels=X_train_scaled_df.index, distance_sort='descending', show_leaf_counts=True)
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('Sample Index')
plt.ylabel('Distance')
plt.show()


pca = PCA()

# Fit the PCA model
X_pca = pca.fit_transform(X_train_scaled_df)

# Explained variance ratio
explained_variance_ratio = pca.explained_variance_ratio_

# cumulative explained variance
cumulative_explained_variance = explained_variance_ratio.cumsum()

plt.figure(figsize=(10, 6))
plt.plot(range(1, len(cumulative_explained_variance) + 1), cumulative_explained_variance, marker='o', linestyle='--')
plt.title('Cumulative Explained Variance by Principal Components')
plt.xlabel('Number of Principal Components')
plt.ylabel('Cumulative Explained Variance')
plt.grid(True)
plt.show()


pca.explained_variance_ratio_

array([0.37549092, 0.18809403, 0.11252276, 0.10541261, 0.06670798,
       0.05364076, 0.04507275, 0.02547366, 0.0168875 , 0.01069703])


pca = PCA(n_components=7)
X_pca = pca.fit_transform(X_train_scaled_df)


num_clusters = 2

# Apply KMeans clustering
kmeans = KMeans(n_clusters=num_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(X_pca)

# Calculate silhouette score
silhouette_score(X_pca, cluster_labels)

C:\Users\Dell\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.
  warnings.warn(

0.2654924246544464


y_train = y_train.values.ravel()
y_test = y_test.values.ravel()

logreg = LogisticRegression(random_state=42)
logreg.fit(X_train_scaled_df, y_train)

LogisticRegression(random_state=42)

LogisticRegression(random_state=42)


y_pred = logreg.predict(X_test_scaled_df)


accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)


print(f"Accuracy: {accuracy:.4f}")
print("\nConfusion Matrix:")
print(conf_matrix)
print("\nClassification Report:")
print(classification_rep)

Accuracy: 0.9529

Confusion Matrix:
[[101   5]
 [  3  61]]

Classification Report:
              precision    recall  f1-score   support

           0       0.97      0.95      0.96       106
           1       0.92      0.95      0.94        64

    accuracy                           0.95       170
   macro avg       0.95      0.95      0.95       170
weighted avg       0.95      0.95      0.95       170


svm_classifier = SVC(kernel='poly', C=50, gamma='scale', random_state=42)
svm_classifier.fit(X_train_scaled_df, y_train)
y_pred = svm_classifier.predict(X_test_scaled_df)

accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)

print(f'Accuracy: {accuracy:.4f}')
print('\nConfusion Matrix:')
print(conf_matrix)
print('\nClassification Report:')
print(class_report)

Accuracy: 0.9235

Confusion Matrix:
[[101   5]
 [  8  56]]

Classification Report:
              precision    recall  f1-score   support

           0       0.93      0.95      0.94       106
           1       0.92      0.88      0.90        64

    accuracy                           0.92       170
   macro avg       0.92      0.91      0.92       170
weighted avg       0.92      0.92      0.92       170


clf = RandomForestClassifier(class_weight='balanced')
clf.fit(X_train_scaled_df, y_train)

y_pred = clf.predict(X_test_scaled_df)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)

# Print the results
print(f"Accuracy: {accuracy:.4f}")
print("Classification Report:\n", classification_rep)

Accuracy: 0.9412
Classification Report:
               precision    recall  f1-score   support

           0       0.94      0.97      0.95       106
           1       0.95      0.89      0.92        64

    accuracy                           0.94       170
   macro avg       0.94      0.93      0.94       170
weighted avg       0.94      0.94      0.94       170


param_grid = {
    'C': [0.1, 1, 10, 100],  # Values of regularization parameter C
    'gamma': ['scale', 'auto', 0.1, 0.01],  # Values of gamma parameter
    'kernel': ['rbf', 'linear', 'poly', 'sigmoid']
}

svm_classifier2 = SVC(kernel='rbf', random_state=42)

stratified_kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

# Create the grid search object
grid_search = GridSearchCV(svm_classifier, param_grid, cv=stratified_kfold, scoring='accuracy')

# Perform the grid search on the training data
grid_search.fit(X_train_scaled_df, y_train)

# Get the best parameters and the best model
best_params = grid_search.best_params_
best_svm_model = grid_search.best_estimator_

# Print the best parameters
print(f'Best Parameters: {best_params}')

y_pred = best_svm_model.predict(X_test_scaled_df)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)

print(f'Accuracy: {accuracy:.4f}')
print('\nConfusion Matrix:')
print(conf_matrix)
print('\nClassification Report:')
print(class_report)

Best Parameters: {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}
Accuracy: 0.9529

Confusion Matrix:
[[102   4]
 [  4  60]]

Classification Report:
              precision    recall  f1-score   support

           0       0.96      0.96      0.96       106
           1       0.94      0.94      0.94        64

    accuracy                           0.95       170
   macro avg       0.95      0.95      0.95       170
weighted avg       0.95      0.95      0.95       170


rf_classifier = RandomForestClassifier(random_state=42)

# Define the hyperparameters grid to search
param_grid = {
    'n_estimators': [100, 200, 300],  # Number of trees in the forest
    'max_depth': [None, 10, 20, 30],   # Maximum depth of the trees
    'min_samples_split': [2, 5, 10],   # Minimum number of samples required to split a node
    'min_samples_leaf': [1, 2, 4]      # Minimum number of samples required at each leaf node
}

# Perform grid search cross-validation
grid_search = GridSearchCV(estimator=rf_classifier, param_grid=param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train_scaled_df, y_train)

# Get the best parameters and the best model
best_params = grid_search.best_params_
best_rf_model = grid_search.best_estimator_

# Print the best parameters
print(f'Best Parameters: {best_params}')

# Evaluate the best model on the test set
accuracy = best_rf_model.score(X_test_scaled_df, y_test)
print(f'Accuracy on Test Set: {accuracy}')

Best Parameters: {'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 10, 'n_estimators': 100}
Accuracy on Test Set: 0.9294117647058824

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	842302.0	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	...	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	842517.0	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	...	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
2	84300903.0	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	...	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
3	84348301.0	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	...	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
4	84358402.0	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	...	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
566	926682.0	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	...	23.690	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637
567	926954.0	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	...	18.980	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820
568	927241.0	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	...	25.740	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400
569	92751.0	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	...	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039
570	92751.0	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	...	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039

	id	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	5.680000e+02	566.000000	565.000000	567.000000	566.000000	568.000000	567.000000	567.000000	563.000000	568.000000	...	558.000000	550.000000	565.000000	567.000000	562.000000	567.000000	568.000000	565.000000	567.000000	558.000000
mean	3.011402e+07	14.103267	-241.973664	91.949048	654.942403	0.096312	0.104333	0.088712	-3.500369	0.187402	...	16.269794	25.735691	110.948035	897.936508	0.132469	0.254412	0.272125	0.114470	0.290327	0.084020
std	1.250894e+08	3.517424	445.216862	24.358029	352.555899	0.014178	0.052878	0.079739	59.492306	0.115008	...	4.842370	6.123776	59.245691	688.231051	0.022865	0.157582	0.208867	0.065854	0.061907	0.018171
min	8.670000e+03	6.981000	-999.000000	43.790000	143.500000	0.052630	0.019380	0.000000	-999.000000	0.000700	...	7.930000	12.020000	50.410000	185.200000	0.071170	0.027290	0.000000	0.000000	0.156500	0.055040
25%	8.690778e+05	11.692500	-999.000000	75.190000	420.300000	0.086290	0.064710	0.029520	0.019885	0.161900	...	13.015000	21.222500	84.160000	515.550000	0.116850	0.147450	0.114475	0.064930	0.250450	0.071318
50%	9.060010e+05	13.320000	17.000000	86.240000	548.750000	0.095895	0.092630	0.061540	0.033340	0.179200	...	14.965000	25.455000	97.820000	686.600000	0.131350	0.214100	0.227450	0.099930	0.282600	0.079960
75%	8.812852e+06	15.780000	21.010000	104.200000	787.050000	0.105325	0.130400	0.130000	0.073520	0.195700	...	18.782500	29.705000	126.900000	1091.500000	0.146000	0.339500	0.383500	0.161300	0.318550	0.092088
max	9.113205e+08	28.110000	39.280000	188.500000	2501.000000	0.163400	0.345400	0.426800	0.201200	2.100000	...	36.040000	49.540000	910.000000	10056.000000	0.222600	1.058000	1.252000	0.291000	0.663800	0.207500

	radius_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	radius_se	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	...	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000	396.000000
mean	14.124184	92.041174	656.806313	0.096245	0.103857	0.088022	0.048740	0.184829	0.062728	0.405416	...	16.283543	25.481957	112.358788	902.990530	0.131886	0.250699	0.268464	0.114638	0.287784	0.083747
std	3.508893	24.309240	358.533297	0.014342	0.051733	0.077712	0.037691	0.100778	0.007034	0.290624	...	4.828584	6.120742	67.117134	738.143812	0.023058	0.152696	0.196111	0.062746	0.062492	0.017705
min	7.729000	47.980000	178.800000	0.062510	0.019380	0.000692	0.001852	0.000700	0.049960	0.111500	...	9.077000	12.020000	57.170000	248.000000	0.081250	0.034320	0.001845	0.008772	0.156500	0.055040
25%	11.697500	75.127500	420.175000	0.085402	0.062805	0.030825	0.020865	0.161875	0.057655	0.232250	...	13.067500	20.910000	84.567500	516.325000	0.114625	0.147775	0.121800	0.065713	0.247250	0.071733
50%	13.290000	85.935000	546.350000	0.094895	0.092350	0.059105	0.033770	0.178800	0.061820	0.321750	...	14.940000	25.050000	98.320000	687.100000	0.129800	0.209250	0.219350	0.098095	0.280500	0.080075
75%	15.715000	103.325000	766.950000	0.105150	0.131075	0.129950	0.070387	0.195450	0.066028	0.482725	...	18.295000	29.410000	123.700000	1026.750000	0.146000	0.341700	0.385300	0.157850	0.317800	0.091360
max	28.110000	188.500000	2501.000000	0.163400	0.286700	0.426800	0.201200	2.100000	0.097440	2.873000	...	36.040000	49.540000	910.000000	10056.000000	0.218400	0.937900	0.938700	0.290300	0.663800	0.173000

	radius_mean	perimeter_mean	area_mean	compactness_mean	concavity_mean	concave points_mean	fractal_dimension_mean	radius_se	texture_se	area_se	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
diagnosis
B	12.180	78.235	455.8	0.075145	0.03735	0.02396	0.061885	0.2561	1.093	19.41	...	13.36	22.775	87.3	548.45	0.12375	0.1698	0.1423	0.07625	0.2645	0.07732
M	17.385	114.400	947.9	0.133900	0.14900	0.08520	0.061400	0.5472	1.153	58.38	...	20.47	28.220	136.1	1295.00	0.14325	0.3583	0.4098	0.17720	0.3103	0.08701

LOADING THE DATA

DATA PREPROCESSING AND EDA

Introduction

HANDLING MISSING VALUES AND DUPLICATE VALUES

ENCODING

SKEWNESS AND TRANSFORMATIONS(BOXCOX)

PROBABILTY PLOT

FEATURE SCALING(STANDARDIZATION)

FEATURE REDUCTION

CORRELATION ANALYSIS.

SUMMARY

UNSUPERVISED MACHINE LEARNING ANALYSIS

Dendogram

PRINCIPAL COMPONENT ANALYSIS

SUMMARY

CLASSIFICATION

HYPER-PARAMETER TUNING

CONCLUSION

RECOMMENDATIONS

Ethical Considerations in Machine Learning

REFERENCES

	radius_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	radius_se	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	...	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000	170.000000
mean	14.107482	91.757176	651.869412	0.096703	0.105484	0.093228	0.051170	0.193420	0.062788	0.403829	...	16.243035	26.112206	107.358235	884.703529	0.133891	0.263809	0.293240	0.120894	0.295912	0.084613
std	3.519035	24.317278	337.281725	0.013493	0.055562	0.081974	0.039562	0.143452	0.006588	0.245812	...	4.802858	5.891524	33.992053	556.201128	0.021960	0.168447	0.226173	0.065608	0.060482	0.018853
min	6.981000	43.790000	143.500000	0.052630	0.023440	0.001194	0.002404	0.134200	0.050240	0.114400	...	7.930000	12.490000	50.410000	185.200000	0.071170	0.027290	0.003581	0.010420	0.189000	0.055210
25%	11.717500	75.317500	426.500000	0.087470	0.067835	0.035295	0.021895	0.163500	0.058335	0.235075	...	13.015000	22.025000	83.922500	511.175000	0.121150	0.147450	0.140500	0.072683	0.255625	0.071887
50%	13.375000	86.415000	552.300000	0.097165	0.094490	0.064900	0.033065	0.179400	0.061280	0.322000	...	14.885000	25.765000	97.385000	667.450000	0.134800	0.226250	0.255650	0.104250	0.288250	0.079545
75%	16.122500	106.075000	799.450000	0.106075	0.129125	0.129825	0.078985	0.197450	0.065960	0.469150	...	19.355000	29.922500	129.175000	1162.750000	0.145800	0.321925	0.381950	0.174550	0.319175	0.095090
max	25.220000	171.500000	1878.000000	0.132600	0.345400	0.426400	0.184500	2.020000	0.082430	1.292000	...	30.750000	42.790000	211.700000	3143.000000	0.222600	1.058000	1.252000	0.291000	0.544000	0.207500

	radius_mean	perimeter_mean	area_mean	compactness_mean	concavity_mean	concave points_mean	fractal_dimension_mean	radius_se	texture_se	area_se	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
diagnosis
B	12.23	78.19	461.15	0.07558	0.03804	0.023960	0.061205	0.25995	1.1545	19.280	...	13.355	23.055	86.635	547.4	0.12765	0.17040	0.15615	0.076285	0.27715	0.076790
M	17.25	112.40	928.85	0.13115	0.15135	0.086055	0.062075	0.53620	1.0480	56.395	...	20.470	28.995	140.900	1316.5	0.14430	0.34205	0.40170	0.184450	0.31145	0.091315

	Feature	Original Skewness (Train)	Transformed Skewness (Train)	Original Skewness (Test)	Transformed Skewness (Test)
0	symmetry_mean	17.386063	-1.155954	12.248952	2.425155
1	radius_se	3.445799	0.201723	1.568552	0.181072
2	perimeter_se	3.861897	0.071935	1.764796	0.131616
3	area_se	5.939008	-0.052221	2.086565	0.320258
4	concavity_se	5.594148	0.163786	4.529782	0.281627
5	fractal_dimension_se	19.714879	1.414724	2.386084	0.923502
6	perimeter_worst	7.752660	0.013088	0.906287	-0.446022
7	area_worst	5.740575	0.028833	1.405332	-0.293219

	diagnosis
141	1
302	1
451	1
472	0
19	0

	diagnosis
543	0
528	0
234	0
529	0
492	1